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Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10(-15)), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10(-6)). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk. peerReviewed

Purpose: This study aimed to quantify the probability of overdiagnosis of prostate cancer by polygenic risk. Genet Med 17 10, 789–795. Methods: We calculated the polygenic risk score based on 66 known prostate cancer susceptibility variants for 17,012 men aged 50–69 years (9,404 men identified with prostate cancer and 7,608 with no cancer) derived from three UK-based ongoing studies. We derived the probabilities of overdiagnosis by quartiles of polygenic risk considering that the observed prevalence of screen-detected prostate cancer is a combination of underlying incidence, mean sojourn time (MST), test sensitivity, and overdiagnosis. Genet Med 17 10, 789–795. Results: Polygenic risk quartiles 1 to 4 comprised 9, 18, 25, and 48% of the cases, respectively. For a prostate-specific antigen test sensitivity of 80% and MST of 9 years, 43, 30, 25, and 19% of the prevalent screen-detected cancers in quartiles 1 to 4, respectively, were likely to be overdiagnosed cancers. Overdiagnosis decreased with increasing polygenic risk, with 56% decrease between the lowest and the highest polygenic risk quartiles. Genet Med 17 10, 789–795. Conclusion: Targeting screening to men at higher polygenic risk could reduce the problem of overdiagnosis and lead to a better benefit-to-harm balance in screening for prostate cancer. Genet Med 17 10, 789–795.

Background: Common cancers develop through a multistep process often including inherited susceptibility. Collaboration among multiple institutions, and funding from multiple sources, has allowed the development of an inexpensive genotyping microarray, the OncoArray. The array includes a genome-wide backbone, comprising 230,000 SNPs tagging most common genetic variants, together with dense mapping of known susceptibility regions, rare variants from sequencing experiments, pharmacogenetic markers and cancer related traits. Methods: The OncoArray can be genotyped using a novel technology developed by Illumina to facilitate efficient genotyping. The consortium developed standard approaches for selecting SNPs for study, for quality control of markers and for ancestry analysis. The array was genotyped at selected sites and with prespecified replicate samples to permit evaluation of genotyping accuracy among centers and by ethnic background. Results: The OncoArray consortium genotyped 447,705 samples. A total of 494,763 SNPs passed quality control steps with a sample success rate of 97% of the samples. Participating sites performed ancestry analysis using a common set of markers and a scoring algorithm based on principal components analysis. Conclusions: Results from these analyses will enable researchers to identify new susceptibility loci, perform fine mapping of new or known loci associated with either single or multiple cancers, assess the degree of overlap in cancer causation and pleiotropic effects of loci that have been identified for disease-specific risk, and jointly model genetic, environmental and lifestyle related exposures. Impact: Ongoing analyses will shed light on etiology and risk assessment for many types of cancer. TRICL (Transdisciplinary Research for Cancer of Lung) and International Lung Cancer Consortium (ILCCO): National Institute of Health U19 CA148127-01 (C.I. Amos, J. Byun, Y. Li, X. Xiao, J. L. Forman, A. Risch, H. Bickeböller, M. Waldenberger, I. Brüske, J. Manz, P. Brennan, R. Hung, H. Shen, Y. Shi, A. Kamal, C. I. Amos, J.K. Field), Canadian Cancer Society Research Institute (no. 020214, R. Hung). DRIVE (Discovery, Biology, and Risk of Inherited Variants in Breast Cancer): National Institute of Health U19 CA148065 (D.J. Hazelett, D. F. Easton, S. Lindström, P. Kraft, J. Dennis, A. Dunning, K. Michailidou, L. Fachal, S. Benlloch, J. Cunningham, K. Butterbach, K. Offit, R. Schmutzler, L. Ottini, D. Vincent). CORECT (ColoRectal Transdisciplinary Study): National Institute of Health U19 CA148107; R01 CA81488, P30 CA014089 (S. Gruber, U. Peters, G. Casey). ELLIPSE (ELLIPSE, Elucidating Loci in Prostate Cancer Susceptibility): This work was support by U19 CA148537(F. Schumacher, S.A. Gayther, S.L. Schmit, C.K. Edlund, D.J. Hazelett, G.A. Coetzee, C. Haiman, S. Demetriades, D. Van Den Berg). FOCI (Transdisciplinary Cancer Genetic Association and Interacting Studies): National Institutes of Health U19 CA148112- 01 (T.A. Sellers, C. Phelan), R01-CA122443(B. Hicks) ) P50-CA116201, CA192393 (F. Couch), P50-CA136393(J. Cunningham, F. Couch), OCAC - CA-149429 (C. Phelan). , P30-CA15083 (B. Hicks), Cancer Research UK (C490/A8339(A. Antoniou, P. Pharoah, A. Dunning), C490/A16561(P. Pharoah, A. Dunning), C490/A10119 (A. Dunning, P. Pharoah), C490/A10124 (A. Dunning, P. Pharoah)). ASTERISK: a Hospital Clinical Research Program (PHRC) and supported by the Regional Council of Pays de la Loire, the Groupement des Entreprises Françaises dans la Lutte contre le Cancer (GEFLUC), the Association Anne de Bretagne Génétique and the Ligue Régionale Contre le Cancer (LRCC) (F. Bacot). DACHS: German Research Council (Deutsche Downloaded from cebp.aacrjournals.org on October 31, 2016. © 2016 American Association for Cancer Research. Author manuscripts have been peer reviewed and accepted for publication but have not yet been edited. Author Manuscript Published OnlineFirst on October 3, 2016; DOI: 10.1158/1055-9965.EPI-16-0106 Forschungsgemeinschaft, BR 1704/6-1, BR 1704/6-3, BR 1704/6-4, and CH 117/1-1, C.K. Edlund), and the German Federal Ministry of Education and Research (01KH0404 and 01ER0814). Prostate Lung Colon Ovarian Screening trial: Intramural Research Program of the Division of Cancer Epidemiology and Genetics and supported by contracts from the Division of Cancer Prevention, National Cancer Institute, NIH, DHHS. The GC-HBOC (German Consortium of Hereditary Breast and Ovarian Cancer) is supported by the German Cancer Aid (grant no 110837, coordinator: R. Schmutzler, Cologne). The PERSPECTIVE (J. Simard) project was supported by the Government of Canada through Genome Canada and the Canadian Institutes of Health Research (MOP-86727, L.E. Kelemen), the Ministère de l’Économie, Innovation et Exportation du Québec through Genome Québec, and thee Quebec Breast Cancer Foundation, including additional funding from the Canadian Breast Cancer Foundation and the National Cancer Institute USA through Breast Cancer Family Registry Cohort (#1UM1CA164920-01A1). European Union Framework Programme for Research and Innovation (MSCA-IF-2014-EF-656144)(L. Fachal). The Breast Cancer Research Foundation, P30 CA08748, Andrew Sabin Foundation (K. Offit). CIDR genotyping for the Oncoarray was conducted under contract 268201200008I to K.Doheny and through grant 1X01HG007491- 01 to C.I. Amos. This is the author accepted manuscript. The final version is available from American Association for Cancer Research via http://dx.doi.org/10.1158/1055-9965.EPI-16-0106

Genetic risk scores were used as unconfounded instruments for specific lipid traits (Mendelian randomization) to assess whether circulating lipids causally influence prostate cancer risk. Data from 22,249 prostate cancer cases and 22,133 controls from 22 studies within the international PRACTICAL consortium were analyzed. Allele scores based on single nucleotide polymorphisms (SNPs) previously reported to be uniquely associated with each of low-density lipoprotein (LDL), high-density lipoprotein (HDL), and triglyceride (TG) levels, were first validated in an independent dataset, and then entered into logistic regression models to estimate the presence (and direction) of any causal effect of each lipid trait on prostate cancer risk. There was weak evidence for an association between the LDL genetic score and cancer grade: the odds ratio (OR) per genetically instrumented standard deviation (SD) in LDL, comparing high- (≥7 Gleason score) versus low-grade (

Breast, ovarian, and prostate cancers are hormone-related and may have a shared genetic basis, but this has not been investigated systematically by genome-wide association (GWA) studies. Meta-analyses combining the largest GWA meta-analysis data sets for these cancers totaling 112,349 cases and 116,421 controls of European ancestry, all together and in pairs, identified at P < 10−8 seven new cross-cancer loci: three associated with susceptibility to all three cancers (rs17041869/2q13/BCL2L11; rs7937840/11q12/INCENP; rs1469713/19p13/GATAD2A), two breast and ovarian cancer risk loci (rs200182588/9q31/SMC2; rs8037137/15q26/RCCD1), and two breast and prostate cancer risk loci (rs5013329/1p34/NSUN4; rs9375701/6q23/L3MBTL3). Index variants in five additional regions previously associated with only one cancer also showed clear association with a second cancer type. Cell-type–specific expression quantitative trait locus and enhancer–gene interaction annotations suggested target genes with potential cross-cancer roles at the new loci. Pathway analysis revealed significant enrichment of death receptor signaling genes near loci with P < 10−5 in the three-cancer meta-analysis. Significance: We demonstrate that combining large-scale GWA meta-analysis findings across cancer types can identify completely new risk loci common to breast, ovarian, and prostate cancers. We show that the identification of such cross-cancer risk loci has the potential to shed new light on the shared biology underlying these hormone-related cancers. Cancer Discov; 6(9); 1052–67. ©2016 AACR. This article is highlighted in the In This Issue feature, p. 932

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same region

Purpose BRCA1/2 mutations increase the risk of breast and prostate cancer in men. Common genetic variants modify cancer risks for female carriers of BRCA1/2 mutations. We investigated-for the first time to our knowledge-associations of common genetic variants with breast and prostate cancer risks for male carriers of BRCA1/ 2 mutations and implications for cancer risk prediction. Materials and Methods We genotyped 1,802 male carriers of BRCA1/2 mutations from the Consortium of Investigators of Modifiers of BRCA1/2 by using the custom Illumina OncoArray. We investigated the combined effects of established breast and prostate cancer susceptibility variants on cancer risks for male carriers of BRCA1/2 mutations by constructing weighted polygenic risk scores (PRSs) using published effect estimates as weights. Results In male carriers of BRCA1/2 mutations, PRS that was based on 88 female breast cancer susceptibility variants was associated with breast cancer risk (odds ratio per standard deviation of PRS, 1.36; 95% CI, 1.19 to 1.56; P = 8.6 × 10(-6)). Similarly, PRS that was based on 103 prostate cancer susceptibility variants was associated with prostate cancer risk (odds ratio per SD of PRS, 1.56; 95% CI, 1.35 to 1.81; P = 3.2 × 10(-9)). Large differences in absolute cancer risks were observed at the extremes of the PRS distribution. For example, prostate cancer risk by age 80 years at the 5th and 95th percentiles of the PRS varies from 7% to 26% for carriers of BRCA1 mutations and from 19% to 61% for carriers of BRCA2 mutations, respectively. Conclusion PRSs may provide informative cancer risk stratification for male carriers of BRCA1/2 mutations that might enable these men and their physicians to make informed decisions on the type and timing of breast and prostate cancer risk management.

Genome-wide association studies (GWAS) have identified 76 variants associated with prostate cancer risk predominantly in populations of European ancestry. To identify additional susceptibility loci for this common cancer, we conducted a meta-analysis of > 10 million SNPs in 43,303 prostate cancer cases and 43,737 controls from studies in populations of European, African, Japanese and Latino ancestry. Twenty-three new susceptibility loci were identified at association P < 5 × 10(-8); 15 variants were identified among men of European ancestry, 7 were identified in multi-ancestry analyses and 1 was associated with early-onset prostate cancer. These 23 variants, in combination with known prostate cancer risk variants, explain 33% of the familial risk for this disease in European-ancestry populations. These findings provide new regions for investigation into the pathogenesis of prostate cancer and demonstrate the usefulness of combining ancestrally diverse populations to discover risk loci for disease.

What's known on the subject? and What does the study add? Family history (FH) is a major risk factor for the development of prostate cancer. The search for genetic variants has led to genome-wide association studies (GWAS), which have so far reported 47 susceptibility loci that predispose men to prostate cancer. However, the use of genetics or FH status in predicting clinical outcomes after prostate cancer diagnosis remains uncertain. Guidelines currently exist for clinicians and patients summarising evidence relating to the best outcomes of different prostate cancer treatment methods. Genetics and FH could potentially add to this stratification. Our study aimed to ascertain the potential prognostic roles of FH or genetic risk scores in patients managed by active surveillance. Objectives To explore the potential prognostic role of family history (FH) of prostate cancer and prostate cancer risk single nucleotide polymorphisms (SNPs) in patients undergoing active surveillance (AS) for prostate cancer. This is the first study to date, which has investigated the potential prognostic role of SNP profiles in an AS cohort Patients and Methods FH data were collected from patients in the Royal Marsden Hospital AS study. In all, 39 prostate cancer-risk SNPs identified from published genome wide association studies (GWAS) were genotyped using the Sequenom Platform and TaqMan™ assays from available DNA. The cumulative genetic-risk scores for each patient were then calculated using the weighted effect estimated from previous GWAS (log-additive model). FH status and the genetic-risk scores were assessed against adverse outcomes in AS, time to treatment and adverse histology on repeat biopsy, using univariable and multivariable Cox regression models to address time to treatment; and binary logistic regression to address biopsy upgrade. Results Of 471 patients, 55 (13.6%) had adverse histology on repeat biopsies and 145 (30.8%) had deferred treatment. On univariate analysis, there was no significant relationship between FH of prostate cancer in any degree of relation, and adverse histology or time to treatment. For risk score analyses, 386 patients' DNA was studied; and there was also no relationship found between the calculated genetic risk scores and adverse histology or time to treatment (P = 0.573 and P = 0.965, respectively). The retrospective study design and the few events were the main limitation of the study. Conclusions There is currently insufficient data to support the use of FH status or prostate cancer SNP profile risk scores as prognostic factors in AS and these should not be used to influence management decisions. As more genetic variants are discovered this may change and should be reassessed in multicentre AS cohorts.

Worldwide, familial and epidemiological studies have generated considerable evidence of an inherited component to prostate cancer. Indeed, rare highly penetrant genetic mutations have been implicated. Genome-wide association studies (GWAS) have also identified 76 susceptibility loci associated with prostate cancer risk, which occur commonly but are of low penetrance. However, these mutations interact multiplicatively, which can result in substantially increased risk. Currently, approximately 30% of the familial risk is due to such variants. Evaluating the functional aspects of these variants would contribute to our understanding of prostate cancer aetiology and would enable population risk stratification for screening. Furthermore, understanding the genetic risks of prostate cancer might inform predictions of treatment responses and toxicities, with the goal of personalized therapy. However, risk modelling and clinical translational research are needed before we can translate risk profiles generated from these variants into use in the clinical setting for targeted screening and treatment.

BackgroundProstate-specific-antigen (PSA) screening resulted in reduced prostate cancer (PCa) mortality in a large clinical trial, but due to a high false-positive rate, among other concerns, many guidelines do not endorse universal screening and instead recommend an individualized decision based on each patient’s risk. Genetic risk may provide key information to guide the decisions of whether and at what age to screen an individual man for PCa.MethodsGenotype, PCa status, and age from 34,444 men of European ancestry from the PRACTICAL consortium database were analyzed to select single-nucleotide polymorphisms (SNPs) associated with prostate cancer diagnosis. These SNPs were then incorporated into a survival analysis to estimate their effects on age at PCa diagnosis. The resulting polygenic hazard score (PHS) is an assessment of individual genetic risk. The final model was validated in an independent dataset comprised of 6,417 men with screening PSA and genotype data. PHS was calculated for these men to test for prediction of PCa-free survival. PHS was also combined with age-specific PCa incidence data from the U.S. population to generate a PCa-Risk (PCaR) age that relates a given man’s risk to that of the population average. PHS and PCaR age were evaluated for prediction of positive predictive value (PPV) of PSA screening.FindingsPHS calculated from 54 SNPs was very highly predictive of age at PCa diagnosis for men in the validation set (p =10−53). PPV of PSA screening varied from 0.18 to 0.52 for men with low and high genetic risk, respectively. PHS modulates PCa-free survival curves by an estimated 20 years between men in the 1st or 99th percentiles of genetic risk.InterpretationPolygenic hazard scores give personalized genetic risk estimates and can inform the decisions of whether and at what age to screen a man for PCa.FundingDepartment of Defense #W81XWH-13-1-0391

Molecular and epidemiological differences have been described between TMPRSS2:ERG fusion-positive and fusion-negative prostate cancer (PrCa). Assuming two molecularly distinct subtypes, we have examined 27 common PrCa risk variants, previously identified in genome-wide association studies, for subtype specific associations in a total of 1221 TMPRSS2:ERG phenotyped PrCa cases. In meta-analyses of a discovery set of 552 cases with TMPRSS2:ERG data and 7650 unaffected men from five centers we have found support for the hypothesis that several common risk variants are associated with one particular subtype rather than with PrCa in general. Risk variants were analyzed in case-case comparisons (296 TMPRSS2:ERG fusion-positive versus 256 fusion-negative cases) and an independent set of 669 cases with TMPRSS2:ERG data was established to replicate the top five candidates. Significant differences (P < 0.00185) between the two subtypes were observed for rs16901979 (8q24) and rs1859962 (17q24), which were enriched in TMPRSS2:ERG fusion-negative (OR = 0.53, P = 0.0007) and TMPRSS2:ERG fusion-positive PrCa (OR = 1.30, P = 0.0016), respectively. Expression quantitative trait locus analysis was performed to investigate mechanistic links between risk variants, fusion status and target gene mRNA levels. For rs1859962 at 17q24, genotype dependent expression was observed for the candidate target gene SOX9 in TMPRSS2:ERG fusion-positive PrCa, which was not evident in TMPRSS2:ERG negative tumors. The present study established evidence for the first two common PrCa risk variants differentially associated with TMPRSS2:ERG fusion status. TMPRSS2:ERG phenotyping of larger studies is required to determine comprehensive sets of variants with subtype-specific roles in PrCa.

The human tissue Kallikrein family consists of 15 genes with the majority shown to be differentially expressed in cancers and/or indicators of cancer prognosis. We sought to elucidate the role of common genetic variation in four of the Kallikrein genes, KLK5, KLK6, KLK12, and KLK13, in prostate cancer risk and tumor aggressiveness. Genotyping of all 22 tagging single nucleotide polymorphisms (tagSNPs) in the KLK5, KLK6, KLK12, and KLK13 genes was performed in approximately 1,000 prostate cancer cases and 1,300 male controls from Australia. Data from any positive results were also accessed for 1,844 cases and 1,886 controls from a previously published prostate cancer genome-wide association study set from the United Kingdom. For one SNP in KLK12, rs3865443, there was evidence for association with prostate cancer risk of similar direction and magnitude in the replication set to that seen in the Australian cohort. We conducted genotyping of a further 309 prostate cancer cases, and combined analyses revealed an increased risk of prostate cancer for carriers of the rare homozygous genotype for rs3865443 (OR 1.28, 95% CI 1.04-1.57; P = 0.018). No other tagSNPs in the KLK5, KLK6, and KLK13 genes were consistently associated with prostate cancer risk or tumor aggressiveness. Analysis of a combined sample of 3,153 cases and 3,199 controls revealed the KLK12 tagSNP rs3865443 to be marginally statistically significantly associated with risk of prostate cancer. Considering the total number of SNPs investigated in this study, this finding should be interpreted cautiously and requires additional validation from very large datasets such as those of the Prostate Cancer Association group to investigate cancer associated alterations (PRACTICAL) Consortium.

Prostate cancer is the most frequently diagnosed cancer in males in developed countries. To identify common prostate cancer susceptibility alleles, we genotyped 211,155 SNPs on a custom Illumina array (iCOGS) in blood DNA from 25,074 prostate cancer cases and 24,272 controls from the international PRACTICAL Consortium. Twenty-three new prostate cancer susceptibility loci were identified at genome-wide significance (P < 5 × 10(-8)). More than 70 prostate cancer susceptibility loci, explaining ∼30% of the familial risk for this disease, have now been identified. On the basis of combined risks conferred by the new and previously known risk loci, the top 1% of the risk distribution has a 4.7-fold higher risk than the average of the population being profiled. These results will facilitate population risk stratification for clinical studies.

Coffee consumption has been shown in some studies to be associated with lower risk of prostate cancer. However, it is unclear if this association is causal or due to confounding or reverse causality. We conducted a Mendelian randomisation analysis to investigate the causal effects of coffee consumption on prostate cancer risk and progression. We used two genetic variants robustly associated with caffeine intake (rs4410790 and rs2472297) as proxies for coffee consumption in a sample of 46,687 men of European ancestry from 25 studies in the PRACTICAL consortium. Associations between genetic variants and prostate cancer case status, stage and grade were assessed by logistic regression and with all‐cause and prostate cancer‐specific mortality using Cox proportional hazards regression. There was no clear evidence that a genetic risk score combining rs4410790 and rs2472297 was associated with prostate cancer risk (OR per additional coffee increasing allele: 1.01, 95% CI: 0.98,1.03) or having high‐grade compared to low‐grade disease (OR: 1.01, 95% CI: 0.97,1.04). There was some evidence that the genetic risk score was associated with higher odds of having nonlocalised compared to localised stage disease (OR: 1.03, 95% CI: 1.01, 1.06). Amongst men with prostate cancer, there was no clear association between the genetic risk score and all‐cause mortality (HR: 1.00, 95% CI: 0.97,1.04) or prostate cancer‐specific mortality (HR: 1.03, 95% CI: 0.98,1.08). These results, which should have less bias from confounding than observational estimates, are not consistent with a substantial effect of coffee consumption on reducing prostate cancer incidence or progression., What's new? Does coffee consumption reduce prostate cancer risk? It's biologically plausible that it could, but studies showing a link have relied on observational evidence, which could be affected by confounding factors. These authors set out to isolate coffee's contribution. They focused on two genetic variants that correspond with caffeine intake, and used them as proxies for coffee drinking. Alleles are not affected by behavior or demographic factors, nor can behavior changes after diagnosis change whether a person carries an allele. The authors found no correlation between either of the alleles and prostate cancer risk, suggesting drinking coffee may not protect against prostate cancer.

Background: We have developed a genome-wide association study analysis method called DEPTH (DEPendency of association on the number of Top Hits) to identify genomic regions potentially associated with disease by considering overlapping groups of contiguous markers (e.g., SNPs) across the genome. DEPTH is a machine learning algorithm for feature ranking of ultra-high dimensional datasets, built from well-established statistical tools such as bootstrapping, penalized regression, and decision trees. Unlike marginal regression, which considers each SNP individually, the key idea behind DEPTH is to rank groups of SNPs in terms of their joint strength of association with the outcome. Our aim was to compare the performance of DEPTH with that of standard logistic regression analysis. Methods: We selected 1,854 prostate cancer cases and 1,894 controls from the UK for whom 541,129 SNPs were measured using the Illumina Infinium HumanHap550 array. Confirmation was sought using 4,152 cases and 2,874 controls, ascertained from the UK and Australia, for whom 211,155 SNPs were measured using the iCOGS Illumina Infinium array. Results: From the DEPTH analysis, we identified 14 regions associated with prostate cancer risk that had been reported previously, five of which would not have been identified by conventional logistic regression. We also identified 112 novel putative susceptibility regions. Conclusions: DEPTH can reveal new risk-associated regions that would not have been identified using a conventional logistic regression analysis of individual SNPs. Impact: This study demonstrates that the DEPTH algorithm could identify additional genetic susceptibility regions that merit further investigation. Cancer Epidemiol Biomarkers Prev; 25(12); 1619–24. ©2016 AACR.

Background Epidemiological studies have observed a positive association between an earlier age at sexual development and prostate cancer, but markers of sexual maturation in boys are imprecise and observational estimates are likely to suffer from a degree of uncontrolled confounding. To obtain causal estimates, we examined the role of pubertal development in prostate cancer using genetic polymorphisms associated with Tanner stage in adolescent boys in a Mendelian randomization (MR) approach. Methods We derived a weighted genetic risk score for pubertal development, combining 13 SNPs associated with male Tanner stage. A higher score indicated a later puberty onset. We examined the association of this score with prostate cancer risk, stage and grade in the UK-based ProtecT case-control study (n = 2,927), and used the PRACTICAL consortium (n = 43,737) as a replication sample. Results In ProtecT, the puberty genetic score was inversely associated with prostate cancer grade (odds ratio (OR) of high- vs. low-grade cancer, per tertile of the score: 0.76; 95 % CI, 0.64–0.89). In an instrumental variable estimation of the causal OR, later physical development in adolescence (equivalent to a difference of one Tanner stage between pubertal boys of the same age) was associated with a 77 % (95 % CI, 43–91 %) reduced odds of high Gleason prostate cancer. In PRACTICAL, the puberty genetic score was associated with prostate cancer stage (OR of advanced vs. localized cancer, per tertile: 0.95; 95 % CI, 0.91–1.00) and prostate cancer-specific mortality (hazard ratio amongst cases, per tertile: 0.94; 95 % CI, 0.90–0.98), but not with disease grade. Conclusions Older age at sexual maturation is causally linked to a reduced risk of later prostate cancer, especially aggressive disease. Electronic supplementary material The online version of this article (doi:10.1186/s12916-016-0602-x) contains supplementary material, which is available to authorized users.

Genome-wide association studies (GWAS) have revealed numerous genomic 'hits' associated with complex phenotypes. In most cases these hits, along with surrogate genetic variation as measure by numerous single nucleotide polymorphisms (SNPs) that are in linkage disequilibrium, are not in coding genes making assignment of functionality or causality intractable. Here we propose that fine-mapping along with the matching of risk SNPs at chromatin biofeatures lessen this complexity by reducing the number of candidate functional/causal SNPs. For example, we show here that only on average 2 SNPs per prostate cancer risk locus are likely candidates for functionality/causality; we further propose that this manageable number should be taken forward in mechanistic studies. The candidate SNPs can be looked up for each prostate cancer risk region in 2 recent publications in 2015 (1,2) from our groups.

Regulatory authorities have indicated that new drugs to treat type 2 diabetes (T2D) should not be associated with an unacceptable increase in cardiovascular risk. Human genetics may be able to guide development of antidiabetic therapies by predicting cardiovascular and other health endpoints. We therefore investigated the association of variants in six genes that encode drug targets for obesity or T2D with a range of metabolic traits in up to 11, 806 individuals by targeted exome sequencing and follow-up in 39, 979 individuals by targeted genotyping, with additional in silico follow-up in consortia. We used these data to first compare associations of variants in genes encoding drug targets with the effects of pharmacological manipulation of those targets in clinical trials. We then tested the association of those variants with disease outcomes, including coronary heart disease, to predict cardiovascular safety of these agents. A low-frequency missense variant (Ala316Thr; rs10305492) in the gene encoding glucagon-like peptide-1 receptor (GLP1R), the target of GLP1R agonists, was associated with lower fasting glucose and T2D risk, consistent with GLP1R agonist therapies. The minor allele was also associated with protection against heart disease, thus providing evidence that GLP1R agonists are not likely to be associated with an unacceptable increase in cardiovascular risk. Our results provide an encouraging signal that these agents may be associated with benefit, a question currently being addressed in randomized controlled trials. Genetic variants associated with metabolic traits and multiple disease outcomes can be used to validate therapeutic targets at an early stage in the drug development process. Copyright 2016 by the American Association for the Advancement of Science; all rights reserved.

We performed an in-depth and well-powered investigation of genetic variation across the cancer susceptibility region at chromosome 8q24 (127.6-129.0 Mb) to search for novel risk variants associated with prostate cancer (PCa) risk in the European ancestry population. We combined genotyped and imputed data from the PRACTICAL/ELLIPSE OncoArray and iCOGS consortia consisting of 71,535 PrCa cases and 52,935 controls of European ancestry. Variants with high imputation quality score (>0.8) were retained for a total of 5,600 overlapping variants between the two datasets. Associations of genetic variants with PCa risk were evaluated using unconditional logistic regression with adjustment for country and ten principal components. The marginal risk estimates for the 5,600 variants that passed quality control were combined by a fixed effects meta-analysis. A meta-stepwise selection was performed on variants marginally associated with PCa risk from the meta results (P Citation Format: Marco Matejcic, Edward J. Saunders, Tokhir Dadaev, Mark Brook, Ali Amin Al Olama, Fredrick R. Schumacher, Sonja I. Berndt, Sara Benlloch, Kenneth Muir, Koveela Govindasami, Victoria L. Stevens, Susan M. Gapstur, Catherine M. Tangen, Jyotsna Batra, Judith Clements, APCB (Australian Prostate Cancer Bio Resource), Henrik Gronberg, Nora Pashayan, Johanna Schleutker, Demetrius Albanes, Alicja Wolk, Catharine West, Lorelei Mucci, Peter Kraft, Géraldine Cancel-Tassin, Stella Koutros, Karina Dalsgaard Sorensen, Lovise Maehle, Eli Marie Grindedal, Sara Strom, David E. Neal, Freddie C. Hamdy, Jenny L. Donovan, Ruth C. Travis, Robert J. Hamilton, Sue Ann Ingles, Barry Rosenstein, Yong-Jie Lu, Graham G. Giles, Adam S. Kibel, Ana Vega, Jeanette Bensen, Manolis Kogevinas, Fredrik Wiklund, Stephen Chanock, Douglas F. Easton, Rosalind A. Eeles, Zsofia Kote-Jarai, David V. Conti, Christopher A. Haiman. Germline variation at 8q24 and prostate cancer risk in men of European ancestry [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 227.

Prostate cancer (PrCa) is the most frequently diagnosed cancer in males in developed countries. To identify common PrCa susceptibility alleles, we previously conducted a genome-wide association study in which 541,129 SNPs were genotyped in 1,854 PrCa cases with clinically detected disease and in 1,894 controls. We have now extended the study to evaluate promising associations in a second stage in which we genotyped 43,671 SNPs in 3,650 PrCa cases and 3,940 controls and in a third stage involving an additional 16,229 cases and 14,821 controls from 21 studies. In addition to replicating previous associations, we identified seven new prostate cancer susceptibility loci on chromosomes 2, 4, 8, 11 and 22 (with P = 1.6 x 10(-8) to P = 2.7 x 10(-33)).

Background: A recent association study identified a common variant (rs9790517) at 4q24 to be associated with breast cancer risk. Independent association signals and potential functional variants in this locus have not been explored. Methods: We conducted a fine-mapping analysis in 55,540 breast cancer cases and 51,168 controls from the Breast Cancer Association Consortium. Results: Conditional analyses identified two independent association signals among women of European ancestry, represented by rs9790517 [conditional P = 2.51 × 10−4; OR, 1.04; 95% confidence interval (CI), 1.02–1.07] and rs77928427 (P = 1.86 × 10−4; OR, 1.04; 95% CI, 1.02–1.07). Functional annotation using data from the Encyclopedia of DNA Elements (ENCODE) project revealed two putative functional variants, rs62331150 and rs73838678 in linkage disequilibrium (LD) with rs9790517 (r2 ≥ 0.90) residing in the active promoter or enhancer, respectively, of the nearest gene, TET2. Both variants are located in DNase I hypersensitivity and transcription factor–binding sites. Using data from both The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), we showed that rs62331150 was associated with level of expression of TET2 in breast normal and tumor tissue. Conclusion: Our study identified two independent association signals at 4q24 in relation to breast cancer risk and suggested that observed association in this locus may be mediated through the regulation of TET2. Impact: Fine-mapping study with large sample size warranted for identification of independent loci for breast cancer risk. Cancer Epidemiol Biomarkers Prev; 24(11); 1680–91. ©2015 AACR.

BACKGROUND. Polygenic risk scores comprising established susceptibility variants have shown to be informative classifiers for several complex diseases including prostate cancer. For prostate cancer it is unknown if inclusion of genetic markers that have so far not been associated with prostate cancer risk at a genome-wide significant level will improve disease prediction. METHODS. We built polygenic risk scores in a large training set comprising over 25,000 individuals. Initially 65 established prostate cancer susceptibility variants were selected. After LD pruning additional variants were prioritized based on their association with prostate cancer. Six-fold cross validation was performed to assess genetic risk scores and optimize the number of additional variants to be included. The final model was evaluated in an independent study population including 1,370 cases and 1,239 controls. RESULTS. The polygenic risk score with 65 established susceptibility variants provided an area under the curve (AUC) of 0.67. Adding an additional 68 novel variants significantly increased the AUC to 0.68 (P = 0.0012) and the net reclassification index with 0.21 (P = 8.5 E-08). All novel variants were located in genomic regions established as associated with prostate cancer risk. CONCLUSIONS. Inclusion of additional genetic variants from established prostate cancer susceptibility regions improves disease prediction.

Interpretation of biological mechanisms underlying genetic risk associations for prostate cancer is complicated by the relatively large number of risk variants (n = 100) and the thousands of surrogate SNPs in linkage disequilibrium. Here, we combined three distinct approaches: multiethnic fine-mapping, putative functional annotation (based upon epigenetic data and genomeencoded features), and expression quantitative trait loci (eQTL) analyses, in an attempt to reduce this complexity. We examined 67 risk regions using genotyping and imputation-based fine-mapping in populations of European (cases/controls: 8600/6946), African (cases/controls: 5327/5136), Japanese (cases/controls: 2563/4391) and Latino (cases/controls: 1034/1046) ancestry. Markers at 55 regions passed a region-specific significance threshold (P-value cutoff range: 3.9 × 10−4 –5.6 × 10−3 ) and in 30 regions 5604 | Human Molecular Genetics, 2015, Vol. 24, No. 19 we identified markers that were more significantly associated with risk than the previously reported variants in the multiethnic sample. Novel secondary signals (P < 5.0 × 10−6 ) were also detected in two regions (rs13062436/3q21 and rs17181170/3p12). Among 666 variants in the 55 regions with P-values within one order of magnitude of the most-associated marker, 193 variants (29%) in 48 regions overlapped with epigenetic or other putative functional marks. In 11 of the 55 regions, cis-eQTLs were detected with nearby genes. For 12 of the 55 regions (22%), the most significant region-specific, prostate-cancer associated variant represented the strongest candidate functional variant based on our annotations; the number of regions increased to 20 (36%) and 27 (49%) when examining the 2 and 3 most significantly associated variants in each region, respectively. These results have prioritized subsets of candidate variants for downstream functional evaluation.

Background: Unnecessary intervention and overtreatment of indolent disease are common challenges in clinical management of prostate cancer. Improved tools to distinguish lethal from indolent disease are critical. Methods: We performed a genome-wide survival analysis of cause-specific death in 24,023 prostate cancer patients (3,513 disease-specific deaths) from the PRACTICAL and BPC3 consortia. Top findings were assessed for replication in a Norwegian cohort (CONOR). Results: We observed no significant association between genetic variants and prostate cancer survival. Conclusions: Common genetic variants with large impact on prostate cancer survival were not observed in this study. Impact: Future studies should be designed for identification of rare variants with large effect sizes or common variants with small effect sizes. Cancer Epidemiol Biomarkers Prev; 24(11); 1796–800. ©2015 AACR.

Background: Genome-wide association studies have identified multiple genetic variants associated with prostate cancer risk which explain a substantial proportion of familial relative risk. These variants can be used to stratify individuals by their risk of prostate cancer. Methods: We genotyped 25 prostate cancer susceptibility loci in 40,414 individuals and derived a polygenic risk score (PRS). We estimated empirical odds ratios (OR) for prostate cancer associated with different risk strata defined by PRS and derived age-specific absolute risks of developing prostate cancer by PRS stratum and family history. Results: The prostate cancer risk for men in the top 1% of the PRS distribution was 30.6 (95% CI, 16.4–57.3) fold compared with men in the bottom 1%, and 4.2 (95% CI, 3.2–5.5) fold compared with the median risk. The absolute risk of prostate cancer by age of 85 years was 65.8% for a man with family history in the top 1% of the PRS distribution, compared with 3.7% for a man in the bottom 1%. The PRS was only weakly correlated with serum PSA level (correlation = 0.09). Conclusions: Risk profiling can identify men at substantially increased or reduced risk of prostate cancer. The effect size, measured by OR per unit PRS, was higher in men at younger ages and in men with family history of prostate cancer. Incorporating additional newly identified loci into a PRS should improve the predictive value of risk profiles. Impact: We demonstrate that the risk profiling based on SNPs can identify men at substantially increased or reduced risk that could have useful implications for targeted prevention and screening programs. Cancer Epidemiol Biomarkers Prev; 24(7); 1121–9. ©2015 AACR.

Epidemiological studies have reported inconsistent associations between telomere length (TL) and risk for various cancers. These inconsistencies are likely attributable, in part, to biases that arise due to post-diagnostic and post-treatment TL measurement. To avoid such biases, we used a Mendelian randomization approach and estimated associations between nine TL-associated SNPs and risk for five common cancer types (breast, lung, colorectal, ovarian and prostate cancer, including subtypes) using data on 51 725 cases and 62 035 controls. We then used an inverse-variance weighted average of the SNP-specific associations to estimate the association between a genetic score representing long TL and cancer risk. The long TL genetic score was significantly associated with increased risk of lung adenocarcinoma (P = 6.3 × 10−15), even after exclusion of a SNP residing in a known lung cancer susceptibility region (TERT-CLPTM1L) P = 6.6 × 10−6). Under Mendelian randomization assumptions, the association estimate [odds ratio (OR) = 2.78] is interpreted as the OR for lung adenocarcinoma corresponding to a 1000 bp increase in TL. The weighted TL SNP score was not associated with other cancer types or subtypes. Our finding that genetic determinants of long TL increase lung adenocarcinoma risk avoids issues with reverse causality and residual confounding that arise in observational studies of TL and disease risk. Under Mendelian randomization assumptions, our finding suggests that longer TL increases lung adenocarcinoma risk. However, caution regarding this causal interpretation is warranted in light of the potential issue of pleiotropy, and a more general interpretation is that SNPs influencing telomere biology are also implicated in lung adenocarcinoma risk.

The 8q24 region harbors multiple risk variants for distinct cancers including 7 for prostate cancer, the majority of which lie in separate linkage disequilbrium blocks. It is not known whether a common biological mechanism underlies the association of genetic variation with cancer risk at 8q24, or whether there are site-specific functions of regulatory elements that are affected in this region. Given the proximity, the MYC oncogene is a likely candidate as are multiple long non-coding RNAs in the region. To further understand the contribution of germline variation to prostate cancer risk we performed a comprehensive fine-mapping analysis of the region in men of European ancestry from the PRACTICAL/ELLIPSE Consortium. More specifically, we tested 1,731 genotype tag SNPs and 12,221 imputed variants spanning the risk region (127.3-129.0Mb) in 56,363 prostate cancer cases and 37,386 controls of European ancestry that were genotyped with the Illumina OncoArray. We performed stepwise logistic regression and identified 13 variants with risk allele frequencies between 0.006 and 0.998 that surpassed genome-wide statistical significance (p-values between 3.2x10-8 and 8.0 x10-78) and with per allele odds ratios ranging from 1.11(rs5013678) to 2.68(rs183373024). Ongoing analyses that will be presented include incorporating existing GWAS and fine-mapping data (iCOGs) for men of European and African ancestry (35,000 cases and 35,000 controls) using JAM, a Bayesian approach that investigates multi-SNP models using marginal meta-analysis statistics. Leveraging the power from the overall multiethnic meta-analysis (>93,000 cases and >72,000 controls) will provide further insight into the number of independent signals in the region and their contribution to prostate cancer risk in these populations. Citation Format: Kan Wang, Ali Amin Al Olama, Rosalind Eeles, David Conti, Zsofia Kote-Jarai, Christopher A. Haiman. Germline variation at 8q24 and prostate cancer risk [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1311. doi:10.1158/1538-7445.AM2017-1311

Genome-wide association studies (GWAS) have identified more than 160 prostate cancer (PrCa) genetic risk loci, however these variants rarely point directly to the true underlying functional variant driving the association. In this fine-mapping study to narrow the credible causal variant set for 80 PrCa regions representing 89 original independent GWAS signals, we performed Bayesian variable selection in combination with functional annotation and quantile regression. We used imputed data for 83,511 PrCa cases and 62,283 controls investigated with high-density genotyping arrays from the OncoArray, iCOGS and 5 previous GWAS studies from the PRACTICAL/ELLIPSE consortia. To facilitate fine-mapping from one-at-a-time SNP associations meta-analyzed over the consortia we first applied JAM, a novel Bayesian algorithm which searches multi-SNP models in summary data by imputing the correlation structure according to a reference panel. JAM provides inference on the number of independent signals, as well as the set of potential SNPs driving those signals. We utilized functional annotation and eQTL analysis (TCGA prostate tumor data) in combination with quantile regression to further prioritize the most likely causal variants within the credible set of SNPs and identify potential candidate genes and functional mechanisms. The median credible set size from JAM was 17 SNPs per region, shrinking the post-QC input set of variants by about 98%. In 13 regions evidence was found for multiple independent signals, up to a maximum of 5 SNPs. Within the single hit regions, almost half had less than 10 variants selected. In 34 regions the credible set included at least one SNP that was co-localized with a significant eQTL. Quantile regression highlighted enrichment for variants in promoters, DNase hypersensitivity site and eQTLs - representing candidate biological mechanisms underpinning disease development. This study has substantially reduced and prioritized the candidate causal PrCa risk variants within previously known GWAS regions, identifying a small subset of variants for further functional investigation and novel candidate genes at a number of loci. Citation Format: Zsofia Kote-Jarai, Tokhir Dadaev, Ed Saunders, Paul Newcombe, Ezequiel Anokian, Daniel Leongamornlert, Ali Amin Al Olama, Christopher Haiman, Ros Eeles, David Conti, The PRACTICAL/ELLIPSE Consortium. Bayesian fine-mapping using summary data of 145,000 subjects refines common risk associations, discovers secondary signals and novel candidate genes for prostate cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1296. doi:10.1158/1538-7445.AM2017-1296

1 Background: Currently genome-wide association studies (GWAS) have identified over 100 prostate cancer (PrCa) susceptibility loci, capturing 33% of the PrCa familial relative risk (FRR) in Europeans. To identify further susceptibility variants, we conducted a PrCa GWAS, larger than previous studies, comprising ~49,000 cases and ~29,000 controls among individuals of European and Asian descent using the OncoArray, a platform consisting of a 260K GWAS backbone and 310K custom content selected from previous GWAS and fine-mapping studies of multiple cancers ( http://epi.grants.cancer.gov/oncoarray/ ). Methods: Genotypes from the OncoArray were used to impute genotypes from ~70M variants using the October 2014 release of the 1000 genomes project as a reference, and then combined with several previous PrCa GWAS of European ancestry: UK stage 1 (1,906 cases/1,934 controls) and stage 2 (3,888 cases/3,956 controls); CaPS 1 (498 cases/502 controls) and CaPS 2 (1,483 cases/519 controls); BPC3 (2,137 cases/3,101 controls); NCI PEGASUS (4,622 cases/2,954 controls); and iCOGS (21,209 cases/ 20,440 controls). Risk analyses for overall PrCa risk, aggressive PrCa (several definitions defined by PrCa clinical characteristics), and Gleason score were performed. Logistic and linear regression summary statistics were meta-analysed using an inverse variance fixed effect approach. Results: We identified novel loci significantly associated ( P < 5.0x10-8) with overall PrCa (N = 65). Our novel findings are comprised of several missense variants, including a SNP in the ATM gene - a key member of the DNA repair pathway. When combined multiplicatively, the 65 novel PrCa loci identified here increases the captured heritability of PrCa, explaining 38.5% of the FRR when combining novel and previously identified PrCa loci. Conclusions: In risk stratification, men in the top 1% of the genetic risk score group have a relative risk of 5.6 fold for developing PrCa compared with the median risk group. These results will improve the utility of genetic risk scores for targeted screening and prevention for prostate cancer.

Prostate cancer is the second most common malignancy among men worldwide. Genome-wide association studies have identified 100 risk variants for prostate cancer, which can explain approximately 33% of the familial risk of the disease. We hypothesized that a comprehensive analysis of genetic variations found within the 3′ untranslated region of genes predicted to affect miRNA binding (miRSNP) can identify additional prostate cancer risk variants. We investigated the association between 2,169 miRSNPs and prostate cancer risk in a large-scale analysis of 22,301 cases and 22,320 controls of European ancestry from 23 participating studies. Twenty-two miRSNPs were associated (P < 2.3 × 10−5) with risk of prostate cancer, 10 of which were within 7 genes previously not mapped by GWAS studies. Further, using miRNA mimics and reporter gene assays, we showed that miR-3162-5p has specific affinity for the KLK3 rs1058205 miRSNP T-allele, whereas miR-370 has greater affinity for the VAMP8 rs1010 miRSNP A-allele, validating their functional role. Significance: Findings from this large association study suggest that a focus on miRSNPs, including functional evaluation, can identify candidate risk loci below currently accepted statistical levels of genome-wide significance. Studies of miRNAs and their interactions with SNPs could provide further insights into the mechanisms of prostate cancer risk. Cancer Discov; 5(4); 368–79. ©2015 AACR. See related commentary by Yousef, p. 351 This article is highlighted in the In This Issue feature, p. 333

Associations between single nucleotide polymorphisms (snps) at 5p15 and multiple cancer types have been reported. we have previously shown evidence for a strong association between prostate cancer (prca) risk and rs2242652 at 5p15, intronic in the telomerase reverse transcriptase (tert) gene that encodes tert. to comprehensively evaluate the association between genetic variation across this region and prca, we performed a fine-mapping analysis by genotyping 134 snps using a custom illumina iselect array or sequenom massarray iplex, followed by imputation of 1094 snps in 22 301 prca cases and 22 320 controls in the practical consortium. multiple stepwise logistic regression analysis identified four signals in the promoter or intronic regions of tert that independently associated with prca risk. gene expression analysis of normal prostate tissue showed evidence that snps within one of these regions also associated with tert expression, providing a potential mechanism for predisposition to disease. © The author 2013. Published by Oxford University Press.

Genome-wide association studies (GWAS) have identified multiple common genetic variants associated with an increased risk of prostate cancer (PrCa), but these explain less than one-third of the heritability. To identify further susceptibility alleles, we conducted a meta-analysis of four GWAS including 5953 cases of aggressive PrCa and 11 463 controls (men without PrCa). We computed association tests for approximately 2.6 million SNPs and followed up the most significant SNPs by genotyping 49 121 samples in 29 studies through the international PRACTICAL and BPC3 consortia. We not only confirmed the association of a PrCa susceptibility locus, rs11672691 on chromosome 19, but also showed an association with aggressive PrCa [odds ratio = 1.12 (95% confidence interval 1.03-1.21), P = 1.4 × 10(-8)]. This report describes a genetic variant which is associated with aggressive PrCa, which is a type of PrCa associated with a poorer prognosis.

We thank Martens and colleagues ([1][1]) for their comments on our article. We agree that calibration of the model in the extremes of the risk distribution is important. We observed ([2][2]), however, that the estimated ORs for men in the top and bottom 1% of the risk distribution (4.2 and 0.14

Background: There have been few genome-wide association studies (GWAS) of prostate cancer among diverse populations. To search for novel prostate cancer risk variants, we conducted GWAS of prostate cancer in Japanese and Latinos. In addition, we tested prostate cancer risk variants and developed genetic risk models of prostate cancer for Japanese and Latinos. Methods: Our first-stage GWAS of prostate cancer included Japanese (cases/controls = 1,033/1,042) and Latino (cases/controls = 1,043/1,057) from the Multiethnic Cohort (MEC). Significant associations from stage I (P < 1.0 × 10−4) were examined in silico in GWAS of prostate cancer (stage II) in Japanese (cases/controls = 1,583/3,386) and Europeans (cases/controls = 1,854/1,894). Results: No novel stage I single-nucleotide polymorphism (SNP) outside of known risk regions reached genome-wide significance. For Japanese, in stage I, the most notable putative novel association was seen with 10 SNPs (P ≤ 8.0 × 10−6) at chromosome 2q33; however, this was not replicated in stage II. For Latinos, the most significant association was observed with rs17023900 at the known 3p12 risk locus (stage I: OR = 1.45; P = 7.01 × 10−5 and stage II: OR = 1.58; P = 3.05 × 10−7). The majority of the established risk variants for prostate cancer, 79% and 88%, were positively associated with prostate cancer in Japanese and Latinos (stage I), respectively. The cumulative effects of these variants significantly influence prostate cancer risk (OR per allele = 1.10; P = 2.71 × 10−25 and OR = 1.07; P = 1.02 × 10−16 for Japanese and Latinos, respectively). Conclusion and Impact: Our GWAS of prostate cancer did not identify novel genome-wide significant variants. However, our findings show that established risk variants for prostate cancer significantly contribute to risk among Japanese and Latinos. Cancer Epidemiol Biomarkers Prev; 21(11); 2048–58. ©2012 AACR.

Genome wide association studies have identified several single nucleotide polymorphisms ( SNPs) that are independently associated with small increments in risk of prostate cancer, opening up the possibility for using such variants in risk prediction. Using segregation analysis of population-based samples of 4,390 families of prostate cancer patients from the UK and Australia, and assuming all familial aggregation has genetic causes, we previously found that the best model for the genetic susceptibility to prostate cancer was a mixed model of inheritance that included both a recessive major gene component and a polygenic component (P) that represents the effect of a large number of genetic variants each of small effect, where P similar to N(0, sigma(2)(P)). Based on published studies of 26 SNPs that are currently known to be associated with prostate cancer, we have extended our model to incorporate these SNPs by decomposing the polygenic component into two parts: a polygenic component due to the known susceptibility SNPs, P(K) similar to N(0, sigma(2)(K)), and the residual polygenic component due to the postulated but as yet unknown genetic variants, P(U) similar to N(0, sigma(2)(U)). The resulting algorithm can be used for predicting the probability of developing prostate cancer in the future based on both SNP profiles and explicit family history information. This approach can be applied to other diseases for which population-based family data and established risk variants exist. Genet. Epidemiol. 35: 549-556, 2011. (C) 2011Wiley-Liss, Inc.

Prostate cancer (PrCa) is the most common non-skin cancer diagnosed among males in developed countries and the second leading cause of cancer mortality, yet little is known regarding its etiology and factors that influence clinical outcome. Genome-wide association studies (GWAS) of PrCa have identified at least 30 distinct loci associated with small differences in risk. We conducted a GWAS in 2782 advanced PrCa cases (Gleason grade ≥ 8 or tumor stage C/D) and 4458 controls with 571 243 single nucleotide polymorphisms (SNPs). Based on in silico replication of 4679 SNPs (Stage 1, P < 0.02) in two published GWAS with 7358 PrCa cases and 6732 controls, we identified a new susceptibility locus associated with overall PrCa risk at 2q37.3 (rs2292884, P = 4.3 × 10 -8). We also confirmed a locus suggested by an earlier GWAS at 12q13 (rs902774, P = 8.6 × 10 -9). The estimated per-allele odds ratios for these loci (1.14 for rs2292884 and 1.17 for rs902774) did not differ between advanced and non-advanced PrCa (case-only test for heterogeneity P 5 0.72 and P 5 0.61, respectively). Further studies will be needed to assess whether these or other loci are differentially associated with PrCa subtypes. © The Author 2011. Published by Oxford University Press. All rights reserved.

Genome-wide association studies (GWAS) have identified more than 30 prostate cancer (PrCa) susceptibility loci. One of these (rs2735839) is located close to a plausible candidate susceptibility gene, KLK3, which encodes prostate-specific antigen (PSA). PSA is widely used as a biomarker for PrCa detection and disease monitoring. To refine the association between PrCa and variants in this region, we used genotyping data from a two-stage GWAS using samples from the UK and Australia, and the Cancer Genetic Markers of Susceptibility (CGEMS) study. Genotypes were imputed for 197 and 312 single nucleotide polymorphisms (SNPs) from HapMap2 and the 1000 Genome Project, respectively. The most significant association with PrCa was with a previously unidentified SNP, rs17632542 (combined P = 3.9 × 10−22). This association was confirmed by direct genotyping in three stages of the UK/Australian GWAS, involving 10,405 cases and 10,681 controls (combined P = 1.9 × 10−34). rs17632542 is also shown to be associated with PSA levels and it is a non-synonymous coding SNP (Ile179Thr) in KLK3. Using molecular dynamic simulation, we showed evidence that this variant has the potential to introduce alterations in the protein or affect RNA splicing. We propose that rs17632542 may directly influence PrCa risk. Electronic supplementary material The online version of this article (doi:10.1007/s00439-011-0981-1) contains supplementary material, which is available to authorized users.